DeepSDC: Deep Ensemble Learner for the Classification of Social-Media Flooding Events

Hanif, Muhammad Abdullah; Waqas, Muhammad; Muneer, Amgad; Alwadain, Ayed; Tahir, Muhammad Atif; Rafi, Muhammad

doi:10.3390/su15076049

Cited by 8 publications

(1 citation statement)

References 39 publications

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“…Furthermore, mammography is reported to have a sensitivity ranging from 70% to 90% and a false-negative rate of 10% to 30% [3]. It is worth noting that mammography may miss more than 25% of all malignancies [4][5][6][7][8][9][10], particularly in cases where the breasts are too large and complex to distinguish from one another. Therefore, while mammography is a valuable tool, it may only be infallible in some situations.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Breast Cancer Detection through Thermal Imaging and Customized 2D CNN Classifiers

Khan,

Raza,

Muhammad Tanveer Meeran

et al. 2023

VFAST trans. softw. eng.

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Breast cancer is one of the most prevalent and life-threatening forms of cancer due to its aggressive nature and high mortality rates. Early detection significantly improves a patient's chances of survival. Currently, mammography is the preferred diagnostic method, but it has drawbacks such as radiation exposure and high costs. In response to these challenges, thermography has become a less invasive and cost-effective alternative, gaining popularity. We aim to develop a cutting-edge model for breast cancer detection based on thermal imaging. The initial phase involves creating a customized machine-learning (ML) model built on convolutional neural networks (CNN). Subsequently, this model undergoes training using a diverse dataset of thermal images depicting breast abnormalities, enabling it to identify breast cancer effectively. This innovative approach promises to revolutionize breast cancer diagnosis and offers a safer and more accessible alternative to traditional methods. In our recent study, we leveraged thermal image processing techniques to forecast breast cancer precisely based on its external manifestations, particularly in cases where multiple factors are interconnected. This research employed various image classification methods to categorize breast cancer effectively. Our comprehensive approach encompassed segmentation, texture-based feature extraction from thermal images, and subsequent image classification, leading to the successful detection of malignant images. Our study harnessed the power of machine learning to create a tailored classifier, merging key components from GoogleNet, including the utilization of 2D CNNs and activation functions, with the ResNet architecture. This hybrid approach incorporated batch normalization layers following each convolutional layer and employed max-pooling to enhance classification accuracy. Next, we used a sample dataset of carefully selected images from DMR-IR to train our proposed model. The outcomes of this training demonstrated significant improvement over existing methods, with our suggested 2D CNN classifiers achieving an impressive classification rate of 95%, surpassing both the SVM and current CNN models, which achieved rates of 91% and 71%, respectively.

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Section: Introductionmentioning

confidence: 99%

Enhancing Breast Cancer Detection through Thermal Imaging and Customized 2D CNN Classifiers

Khan,

Raza,

Muhammad Tanveer Meeran

et al. 2023

VFAST trans. softw. eng.

View full text Add to dashboard Cite

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Simultaneous instance pooling and bag representation selection approach for multiple-instance learning (MIL) using vision transformer

Waqas,

Tahir,

Author

et al. 2024

Neural Comput & Applic

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In multiple-instance learning (MIL), the existing bag encoding and attention-based pooling approaches assume that the instances in the bag have no relationship among them. This assumption is unsuited, as the instances in the bags are rarely independent in diverse MIL applications. In contrast, the instance relationship assumption-based techniques incorporate the instance relationship information in the classification process. However, in MIL, the bag composition process is complicated, and it may be possible that instances in one bag are related and instances in another bag are not. In present MIL algorithms, this relationship assumption is not explicitly modeled. The learning algorithm is trained based on one of two relationship assumptions (whether instances in all bags have a relationship or not). Hence, it is essential to model the assumption of instance relationships in the bag classification process. This paper proposes a robust approach that generates vector representation for the bag for both assumptions and the representation selection process to determine whether to consider the instances related or unrelated in the bag classification process. This process helps to determine the essential bag representation vector for every individual bag. The proposed method utilizes attention pooling and vision transformer approaches to generate bag representation vectors. Later, the representation selection subnetwork determines the vector representation essential for bag classification in an end-to-end trainable manner. The generalization abilities of the proposed framework are demonstrated through extensive experiments on several benchmark datasets. The experiments demonstrate that the proposed approach outperforms other state-of-the-art MIL approaches in bag classification.

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Exploring Multiple Instance Learning (MIL): A brief survey

Waqas,

Ahmed,

Tahir

et al. 2024

Expert Systems with Applications

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DeepSDC: Deep Ensemble Learner for the Classification of Social-Media Flooding Events

Cited by 8 publications

References 39 publications

Enhancing Breast Cancer Detection through Thermal Imaging and Customized 2D CNN Classifiers

Enhancing Breast Cancer Detection through Thermal Imaging and Customized 2D CNN Classifiers

Simultaneous instance pooling and bag representation selection approach for multiple-instance learning (MIL) using vision transformer

Exploring Multiple Instance Learning (MIL): A brief survey

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